Peptide Microarrays pp 413-430

Part of the Methods in Molecular Biology™ book series (MIMB, volume 570) | Cite as

PASE: A Web-Based Platform for Peptide/Protein Microarray Experiments

  • Fabien Pamelard
  • Gael Even
  • Costin Apostol
  • Cristian Preda
  • Clarisse Dhaenens
  • Vronique Fafeur
  • Rémi Desmet
  • Oleg Melnyk
Protocol

Abstract

Peptide microarray technology requires bioinformatics and statistical tools to manage, store, and analyze the large amount of data produced. To address these needs, we developed a system called protein array software environment (PASE) that provides an integrated framework to manage and analyze microarray information from polypeptide chip technologies.

Key words

Proteomics peptide protein molecular interaction studies database management Web platform and statistical analyses 

References

  1. 1.
    Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J and Vingron M. (2001) Minimum information about a microarray experiment (MIAME) – toward standards for microarray data. Nat Genet. 29(4), 365–71.PubMedCrossRefGoogle Scholar
  2. 2.
  3. 3.
    Lao HS, Carl T, Johan V-C, Sofia G, Åke B and Carsten P. (2002) BioArray Software Environment: A Platform for Comprehensive Management and Analysis of Microarray Data. Genome Biol.3(8): software0003.1–0003.6.Google Scholar
  4. 4.
    Online informations: http://www.genopole-lille.fr/bioinformatique/PASE. Review: 2006, March, 14.
  5. 5.
    Pamelard F, Laurence P, Desmet R, Dhaenens C, Melnyk O and Talbi E-G (2006) PASE: a webbased platform to manage, store and analyze data coming from polypeptide chips experiments, JOBIM conf.Google Scholar
  6. 6.
    Duburcq X, Olivier C, Desmet R, Halasa M, Carion O, Grandidier B, Heim T, Stievenard D, Auriault C and Melnyk O (2004) Polypeptide semicarbazide glass slide microarrays: characterization and comparison with amine slides in serodetection studies on. Bioconjug Chem. 15(2), 317–25.PubMedCrossRefGoogle Scholar
  7. 7.
    Taylor CF, Paton NW, Lilley KS, Binz P-A, Julian RlK Jr, Jones AR, Zhu W, Apweiler R, Aebersold R, Deutsch EW, Dunn MJ, Heck AJR, Leitner A, Macht M, Mann M, Martens L, Neubert TA, Patterson SD, Ping P, Seymour SL, Souda P, Tsugita A, Vandekerckhove J, Vondriska TM, Whitelegge JP, Wilkins MR, Xenarios I, Yates JR and Hermjakob H (2007) The minimum information about a proteomics experiment (MIAPE). Nature Biotechnol. 25, 887–893.CrossRefGoogle Scholar
  8. 8.
    Agrawal R and Srikant R (1994) Fast algorithms for mining association rules. In Proceedings of the 20th Intl. Conference on Very Large Databases,Santiago, Chile, Sept 1994.Google Scholar
  9. 9.
    Khabzaoui M, Dhaenens C, and Talbi E-G (2004) A Multicriteria Genetic Algorithm to analyze DNA microarray data. In Congress on Evolutionary Computation (CEC), volume II, pages 1874–1881, Portland, USA, IEEE Service Center.Google Scholar
  10. 10.
    He Y, Pan W and Lin J (2006) Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data. Comput Stat Data Anal. 51, 641–658.CrossRefGoogle Scholar
  11. 11.
    Celeux G, Martin O and Lavergne C (2005) Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments. Stat Modelling. 5, 243–267.CrossRefGoogle Scholar
  12. 12.
    MacQueen J (1967) Some methods for classification and analysis of multivariate observations, In: LeCam, L.M., Neyman J. (Eds). Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 281–297.Google Scholar
  13. 13.
    Lehmann EL (1959) Testing statistical hypotheses, Wiley, New York.Google Scholar
  14. 14.
    Salvador S and Chan P (2004) Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation, Proc. 16th IEEE Intl. Conf. on Tools with AI, pp. 576–584.Google Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Fabien Pamelard
    • 1
  • Gael Even
    • 1
  • Costin Apostol
    • 1
  • Cristian Preda
    • 1
  • Clarisse Dhaenens
    • 1
  • Vronique Fafeur
    • 2
  • Rémi Desmet
    • 2
  • Oleg Melnyk
    • 3
  1. 1.CIB, Génopole de LilleUniversités de Lille 1 et 2, LIFL, Bâtiment M3, Cité scientifiqueVilleneuve d’Ascq cedexFrance
  2. 2.UMR 8161, CNRS, Universités de Lille 1 et 2Institut Pasteur de Lille, IFR 142 Institut de Biologie de LilleLilleFrance
  3. 3.UMR CNRS 8161 Institut de Biologie de Lille 1Lille CedexFrance

Personalised recommendations